Python Pandas 将堆积列重新格式化为长（？）格式

Question

I have a csv file that looks like this:

And I want it to look like this:

Basically, the country, month, year, and code would repeat, and the Valor export and Volumen export would be unique for the row.. The ending columns can be in any order.

I have tried doing a series of melt, pivot, transpose etc. with no luck. Can any please provide any guidance? Any hints would be greatly appreciated. I am just stumped how to deal with the original csv having the year/month/export value being "stacked"...

Here is the dictionary that can be used to recreate the original csv as a Pandas Data Frame:

pd.DataFrame([{'Unnamed: 0': np.nan, 'Unnamed: 1': np.nan, '2017': 'Enero', 'Unnamed: 3': np.nan, 'Unnamed: 4': 'Febrero', 'Unnamed: 5': np.nan}, {'Unnamed: 0': np.nan, 'Unnamed: 1': np.nan, '2017': 'Valor export', 'Unnamed: 3': 'Volumen export', 'Unnamed: 4': 'Valor export', 'Unnamed: 5': 'Volumen export'}, {'Unnamed: 0': np.nan, 'Unnamed: 1': np.nan, '2017': np.nan, 'Unnamed: 3': np.nan, 'Unnamed: 4': np.nan, 'Unnamed: 5': np.nan}, {'Unnamed: 0': '080390110000 SA-2017', 'Unnamed: 1': 'USA', '2017': '29200.10725', 'Unnamed: 3': '67198.189', 'Unnamed: 4': '38631.16383', 'Unnamed: 5': '87962.196'}, {'Unnamed: 0': '090390110000 SA-2017', 'Unnamed: 1': 'Mexico', '2017': '9283.79255', 'Unnamed: 3': '21638.126', 'Unnamed: 4': '9785.40009', 'Unnamed: 5': '22863.867'}, {'Unnamed: 0': '010390110000 SA-2017 ', 'Unnamed: 1': 'Canada', '2017': '8017.55675', 'Unnamed: 3': '19352.178', 'Unnamed: 4': '11137.27057', 'Unnamed: 5': '27020.428'}, {'Unnamed: 0': '070390110000 SA-2017', 'Unnamed: 1': 'Brazil', '2017': '3786.44363', 'Unnamed: 3': '8704.871', 'Unnamed: 4': '4553.70795', 'Unnamed: 5': '10583.833'}, {'Unnamed: 0': '060390110000 SA-2017', 'Unnamed: 1': 'Italy', '2017': '4809.76636', 'Unnamed: 3': '12411.691', 'Unnamed: 4': '4304.02052', 'Unnamed: 5': '11198.063'}, {'Unnamed: 0': '000390110000 SA-2017 ', 'Unnamed: 1': 'Spain', '2017': '2290.65793', 'Unnamed: 3': '6227.269', 'Unnamed: 4': '3269.41957', 'Unnamed: 5': '9118.595'}, {'Unnamed: 0': '0990390110000 SA-2017 ', 'Unnamed: 1': 'Costa Rica', '2017': '1855.70035', 'Unnamed: 3': '4687.714', 'Unnamed: 4': '2668.57892', 'Unnamed: 5': '6425.365'}, {'Unnamed: 0': '0040390110000 SA-2017 ', 'Unnamed: 1': 'Honduras', '2017': '1823.358', 'Unnamed: 3': '4223.521', 'Unnamed: 4': '250.2036', 'Unnamed: 5': '603.392'}])

Again, any help would be greatly appreciated

Answer 1

Try:

df.iloc[:, 0] = df.iloc[:, 0].str.strip()

idx1 = pd.MultiIndex.from_product(
    [("2017",), ("Enero", "Febrero"), ("Valor Export", "Volumen Export")],
    names=("Year", "Month", "Export"),
)
idx2 = pd.MultiIndex.from_frame(df.iloc[3:, :2], names=("Code", "Country"))

df2 = (
    pd.DataFrame(df.iloc[3:, 2:].values, columns=idx1, index=idx2)
    .stack(level=(0, 1))
    .reset_index()
)
df2.columns.name = None
print(df2.sort_values(by=["Year", "Month"]).to_markdown(index=False))

Prints:

Code	Country	Year	Month	Valor Export	Volumen Export
080390110000 SA-2017	USA	2017	Enero	29200.1	67198.2
090390110000 SA-2017	Mexico	2017	Enero	9283.79	21638.1
010390110000 SA-2017	Canada	2017	Enero	8017.56	19352.2
070390110000 SA-2017	Brazil	2017	Enero	3786.44	8704.87
060390110000 SA-2017	Italy	2017	Enero	4809.77	12411.7
000390110000 SA-2017	Spain	2017	Enero	2290.66	6227.27
0990390110000 SA-2017	Costa Rica	2017	Enero	1855.7	4687.71
0040390110000 SA-2017	Honduras	2017	Enero	1823.36	4223.52
080390110000 SA-2017	USA	2017	Febrero	38631.2	87962.2
090390110000 SA-2017	Mexico	2017	Febrero	9785.4	22863.9
010390110000 SA-2017	Canada	2017	Febrero	11137.3	27020.4
070390110000 SA-2017	Brazil	2017	Febrero	4553.71	10583.8
060390110000 SA-2017	Italy	2017	Febrero	4304.02	11198.1
000390110000 SA-2017	Spain	2017	Febrero	3269.42	9118.59
0990390110000 SA-2017	Costa Rica	2017	Febrero	2668.58	6425.36
0040390110000 SA-2017	Honduras	2017	Febrero	250.204	603.392